Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis

Abstract

Objective To predict response to anti–tumor necrosis factor (anti‐TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti‐TNF treatment. Methods Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response‐associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti‐TNF treatment, and were further validated by a follow‐up study. Results Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up‐regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow‐up study validated the high performance of these models. Conclusion Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti‐TNF treatment.

Tuning Monocytes and Macrophages for Personalized Therapy and Diagnostic Challenge in Rheumatoid Arthritis

“…These data suggest that age-related DNA methylation of monocytes mediates pro-inflammatory cytokines production. Recently, machine learning models generated from transcriptome data of monocytes and DNA methylome data of PBMCs have demonstrated 80.3% accuracy in the prediction rate of RA patients' response to adalimumab, paving the path towards personalized TNF-inhibitor (TNF-i) treatment strategies [46]. Furthermore, significantly increased expression of teneleven translocation1 (TET1) genes was observed in RA monocytes.…”

Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis

“…Guan et al, showed that large collection of clinical data at baseline along with a Gaussian process regression model correctly classified 78% of responder patients (27). In line with this, Tao et al showed the capacity of gene expression and DNA methylation to predict TNFi response in RA using random forest algorithms with an accuracy of 85% (28).…”

Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges

“…Another study identified a limited contribution of genetic markers in addition to clinical parameters in predicting response to anti-TNF therapy in RA using a Gaussian process regression model which correctly classified patients’ response in 78% cases ( Guan et al, 2019 ). A recent ML application for personalised treatment response in RA investigated with success molecular signatures predictive of response to adalimumab and etanercept using differential gene expression in peripheral blood mononuclear cells (PBMCs), monocytes and CD4 + T cells and methylation analysis in PBMCs ( Tao et al, 2021 ). The random forest algorithms implemented to exploit the transcriptome signatures had an overall accuracy of 85.9 and 79% for response to adalimumab and etanercept and they have been validated in a partial dataset (a follow-up study).…”